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Programme

Session ONE: Big Data and Disruptive Innovation

  • Transformation through the convergence of data, technology and talent
  • Creating a data-driven culture
  • Privacy, Security, and Bias in the analysis of Big Data Sets
  • Engaging business leaders with storytelling
  • Building a ‘Greenfield’ data capability at Apetito
  • Case study: unleashing the power of customer analytics
  • Selecting the Core of your Data and Analytics Platform
09.00
Conference Chair’s Opening Address
09.15
Transformation through the convergence of data, technology and talent

Lauren Walker, Chief Operating Officer & Chief Data Officer EMEA, Dentsu Aegis Network

What are the practicalities of delivering on the ambition of delivering customer experience nirvana powered by data?  This question can be answered by sharing lessons learned through a series of experiences in my career driving business transformation through data & tech & a shared strategy.

Ultimately those who are thriving in this new global digital economy are those turning information into a competitive advantage.  It requires more than just tech to get there.  It requires a full change in perspective and new roles and changes agents at all levels of your company to evolve.

  • Using the right lens. So, we’ll explore your unique data lens
  • Recognizing the data defence and the data offensive strategies and how they co-exist
  • Teaching teams to look at data not only as a technical matter but a business matter
  • Having the right team and sponsorship
09.35
CREATING A DATA-DRIVEN CULTURE

Siloed traditional models, inability to understand the immense amount of data, borderline Data IQ offices are growing issues. There is an increasing need for data and analytics leaders to follow the example of English as a second language and treat information as the new second language for business.

  • Data Dexterity – Identifying language gaps and establishing an ISL proof of concept for language development
  • How can leaders (e.g. CDO) become the boosters of curiosity and critical thinking in the workforce
  • How to create office spaces that take advantage of the potential of a data-driven workplace
  • Making data accessible across the enterprise, integrating your data across siloed functions
  • Establishing connections between your data and business objectives
  • Using data to help make informed decisions
09.50
Privacy, Security, and Bias in analysis of Big Data Sets

Dr Louise Bennett, Co-Chair, Privacy and Consumer Advisory Group; Director, Digital Policy Alliance

This talk will consider the key learning points from experience of data analytics over the last 50 years covering:

  • Analytic objectives
  • Data quality and relevance
  • The analyst’s understanding of their data
  • The confusion between correlation and causation
  • Analysis that grows like Topsy
  • Understanding bias
10.05
ENGAGING BUSINESS LEADERS WITH STORYTELLING

Do you want to tell a better story with your data? Would you like your data visualisation to lead to a better understanding of your business and provide some actionable insights? In this session, we consider how data visualisation can be valuable social currency in your enterprise, allowing business functions to share insights and make new discoveries. We discuss:

  • When to use data visualisation
  • Understanding context and target audience
  • Choosing effective visualisation tools
  • Essential criteria of a compelling story
10.20
Building a ‘Greenfield’ data capability at Apetito

Sudesh Jog, Head of Analytics, Apetito

Over the course of three years, Sudesh built the data function at Apetito from scratch.

Based in Wiltshire, Apetito provides nutritious meals for people at home or in hospitals or care homes. The business has rich customer data but was not leveraging the opportunity that this data represented.

In his presentation, Sudesh will talk about the journey of getting data to the heart of strategic decision making at Apetito and will also share his experience and insights about-

  • Developing a data centric culture
  • Getting the buy-in for investment in data
  • Building the team
  • Evolving the technology infrastructure
10.35
Engaging Business Leaders with Storytelling

Do you want to tell a better story with your data? Would you like your data visualisation to lead to a better understanding of your business and provide some actionable insights? In this session, we consider how data visualisation can be a valuable social currency in your enterprise, allowing business functions to share insights and make new discoveries. We discuss:

  • When to use data visualisation
  • Understanding context and target audience
  • Choosing effective visualisation tools
  • Essential criteria of a compelling story
10.50
Questions To The Panel Of Speakers
11.00
Refreshment Break Served in the Exhibition Area
11.30
Case study: unleashing the power of customer analytics

Peter Revill, Data Scientist, comparethemarket.com

Customer analytics is one of the principal drivers of big data analytics adoption. Yet, the sheer variety of potential opportunities and applications to deliver excellent customer experience is overwhelming.

We look at:

  • Key trends and best practices in customer analytics
  • Personalisation and customer journey analytics
  • How partnering with service providers can help you mature your customer analytics
  • Adapting tools and strategies to fit your business (e.g. supply chain and merchandising applications, chatbots, CDP)
  • Whether Customer 360 view is only a utopian panacea
11.45
Selecting the Core of your Data and Analytics Platform

Data is growing at a fast pace, and so have the number of storage options. Data lakes, Data hubs and Data warehouse have similar core functions, and they are often mistakenly understood as interchangeable. The reality is that they usually store different types of data, have different data standards and use diverse data systems. This is why it is essential to pick the right core for your enterprise. We discuss:

  • Understanding the differences between hubs, lakes and warehouses
  • Assessing what fits best for the data you want to store (e.g. flexibility, semantic enablement, size)
  • What are the technology options for each core platform, and how can you integrate them?
12.00
Questions to the Panel of Speakers and Delegates move to the Seminar Rooms
12.15
Seminar Sessions

(To view topics see the seminars page)

13.00
Networking Lunch Served in the Exhibition Area

Session TWO: Making Data the Centrepiece of your Busines

  • From spark to tensorflow: how to build an end to end ML pipeline?
  • Case Study – Discovering Quants: The Wolf Data Scientists of Wall Street
  • Responsibility and transparency in recommendation engines
  • Augmented analytics: from pilot to production
  • Applied data science panel discussion
14.00
Conference Chair’s Afternoon Address
14.05
FROM SPARK TO TENSORFLOW, HOW TO BUILD AN END TO END ML PIPELINE?

Florian Dejax, Data Scientist – Assistant Vice President, Barclaycard

  • Use Spark / Tensorflow to complete some stages in a ML workflow
  • Integrating seamlessly Spark and TensorFlow
  • Use multi GPU to train a deep learning model at scale
14.20
Case Study – Discovering Quants: The Wolf Data Scientists of Wall Street

The need for quantitative finance expertise is increasingly growing, and it is clear that the role of the “quant” – quantitative analysts – has changed significantly. New tasks need new skills. Alternative data, crypto, AI and blockchain have all opened whole new avenues for quantitative analytics to expand.

We explore the challenges/strategies that “quants” face from applying AI and machine learning to a variety of quantitative finance issues (e.g. risk management, trading) to the use of alternative data for forecasting purposes.

14.35
Responsibility and transparency in recommendation engines

Richard Bownes, Data Scientist, BBC

Recommendation engines are built into consumption platforms to improve engagement, increase profits, click through, journey length, viewing duration, etc. In order to make personal recommendations more targeted, these often require some degree of personal information.

At the BBC Datalab, as a public service entity, we have a responsibility to use this data efficiently, transparently and always legally compliantly. Further complicating our mission, we have editorial obligations and considerations in the content we surface.

  • Public service recommendations have a different set of parameters to adhere to
  • A balance between editorial standards, privacy considerations and good recommendations
  • Maintaining a level of trust and transparency while surfacing personally generated recommendations
14.50
AUGMENTED ANALYTICS: I HAVE DATA,NOW WHAT?

Augmented analytics has emerged as a potential solution for this widespread problem of turning vast troves of data into meaningful insights. We explore:

  • What is augmented analytics, and why should I invest in it?
  • What are the roadblocks? Assessing technology challenges, investment risks and lack of skills.
  • Exploring early successful stories (e.g. the medical industry training programmes)
  • Application strategies: extracting complex patterns, semantic indexing, data tagging, simplifying discriminative tasks and more
15.05
Questions to the Panel of Speakers
15.15
Afternoon Networking and Refreshments served in the Exhibition Area
15:45
Applied data science panel discussion

To what extent is data science a “science” to be taught vs. a craft to be practised?

Richard Bownes, Data Scientist, BBC

Rui Hua, Data Scientist, Aberdeen Standard Investments

Florian Dejax, Data Scientist – Assistant Vice President, Barclaycard

Peter Revill, Data Scientist, comparethemarket.com

16.30
Questions to the Panel of Speakers
16.40
Closing Remarks from the Conference Chair
16.45
Conference Closes

Please note:
Whitehall Media reserve the right to change the programme without prior notice.